Overview

Dataset statistics

Number of variables14
Number of observations266
Missing cells169
Missing cells (%)4.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.7 KiB
Average record size in memory225.9 B

Variable types

Categorical2
Numeric12

Alerts

Country Name has a high cardinality: 266 distinct values High cardinality
Country Code has a high cardinality: 266 distinct values High cardinality
1990 is highly correlated with 2000 and 10 other fieldsHigh correlation
2000 is highly correlated with 1990 and 10 other fieldsHigh correlation
2011 is highly correlated with 1990 and 10 other fieldsHigh correlation
2012 is highly correlated with 1990 and 10 other fieldsHigh correlation
2013 is highly correlated with 1990 and 10 other fieldsHigh correlation
2014 is highly correlated with 1990 and 10 other fieldsHigh correlation
2015 is highly correlated with 1990 and 10 other fieldsHigh correlation
2016 is highly correlated with 1990 and 10 other fieldsHigh correlation
2017 is highly correlated with 1990 and 10 other fieldsHigh correlation
2018 is highly correlated with 1990 and 10 other fieldsHigh correlation
2019 is highly correlated with 1990 and 10 other fieldsHigh correlation
2020 is highly correlated with 1990 and 10 other fieldsHigh correlation
1990 is highly correlated with 2000 and 10 other fieldsHigh correlation
2000 is highly correlated with 1990 and 10 other fieldsHigh correlation
2011 is highly correlated with 1990 and 10 other fieldsHigh correlation
2012 is highly correlated with 1990 and 10 other fieldsHigh correlation
2013 is highly correlated with 1990 and 10 other fieldsHigh correlation
2014 is highly correlated with 1990 and 10 other fieldsHigh correlation
2015 is highly correlated with 1990 and 10 other fieldsHigh correlation
2016 is highly correlated with 1990 and 10 other fieldsHigh correlation
2017 is highly correlated with 1990 and 10 other fieldsHigh correlation
2018 is highly correlated with 1990 and 10 other fieldsHigh correlation
2019 is highly correlated with 1990 and 10 other fieldsHigh correlation
2020 is highly correlated with 1990 and 10 other fieldsHigh correlation
1990 is highly correlated with 2000 and 10 other fieldsHigh correlation
2000 is highly correlated with 1990 and 10 other fieldsHigh correlation
2011 is highly correlated with 1990 and 10 other fieldsHigh correlation
2012 is highly correlated with 1990 and 10 other fieldsHigh correlation
2013 is highly correlated with 1990 and 10 other fieldsHigh correlation
2014 is highly correlated with 1990 and 10 other fieldsHigh correlation
2015 is highly correlated with 1990 and 10 other fieldsHigh correlation
2016 is highly correlated with 1990 and 10 other fieldsHigh correlation
2017 is highly correlated with 1990 and 10 other fieldsHigh correlation
2018 is highly correlated with 1990 and 10 other fieldsHigh correlation
2019 is highly correlated with 1990 and 10 other fieldsHigh correlation
2020 is highly correlated with 1990 and 10 other fieldsHigh correlation
1990 is highly correlated with 2000 and 10 other fieldsHigh correlation
2000 is highly correlated with 1990 and 10 other fieldsHigh correlation
2011 is highly correlated with 1990 and 10 other fieldsHigh correlation
2012 is highly correlated with 1990 and 10 other fieldsHigh correlation
2013 is highly correlated with 1990 and 10 other fieldsHigh correlation
2014 is highly correlated with 1990 and 10 other fieldsHigh correlation
2015 is highly correlated with 1990 and 10 other fieldsHigh correlation
2016 is highly correlated with 1990 and 10 other fieldsHigh correlation
2017 is highly correlated with 1990 and 10 other fieldsHigh correlation
2018 is highly correlated with 1990 and 10 other fieldsHigh correlation
2019 is highly correlated with 1990 and 10 other fieldsHigh correlation
2020 is highly correlated with 1990 and 10 other fieldsHigh correlation
1990 has 41 (15.4%) missing values Missing
2000 has 19 (7.1%) missing values Missing
2011 has 8 (3.0%) missing values Missing
2012 has 9 (3.4%) missing values Missing
2013 has 8 (3.0%) missing values Missing
2014 has 8 (3.0%) missing values Missing
2015 has 9 (3.4%) missing values Missing
2016 has 10 (3.8%) missing values Missing
2017 has 10 (3.8%) missing values Missing
2018 has 10 (3.8%) missing values Missing
2019 has 13 (4.9%) missing values Missing
2020 has 24 (9.0%) missing values Missing
Country Name is uniformly distributed Uniform
Country Code is uniformly distributed Uniform
Country Name has unique values Unique
Country Code has unique values Unique

Reproduction

Analysis started2022-04-02 20:23:17.769369
Analysis finished2022-04-02 20:23:35.834714
Duration18.07 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Country Name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct266
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Afghanistan
 
1
St. Lucia
 
1
Serbia
 
1
Seychelles
 
1
Sierra Leone
 
1
Other values (261)
261 

Length

Max length52
Median length9
Mean length12.40225564
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique266 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAmerican Samoa
5th rowAndorra

Common Values

ValueCountFrequency (%)
Afghanistan1
 
0.4%
St. Lucia1
 
0.4%
Serbia1
 
0.4%
Seychelles1
 
0.4%
Sierra Leone1
 
0.4%
Singapore1
 
0.4%
Sint Maarten (Dutch part)1
 
0.4%
Slovak Republic1
 
0.4%
Slovenia1
 
0.4%
Solomon Islands1
 
0.4%
Other values (256)256
96.2%

Length

2022-04-02T15:23:35.922480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20
 
4.0%
and12
 
2.4%
income11
 
2.2%
ida10
 
2.0%
africa9
 
1.8%
islands9
 
1.8%
asia8
 
1.6%
ibrd8
 
1.6%
middle7
 
1.4%
rep7
 
1.4%
Other values (310)404
80.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Country Code
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct266
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
AFG
 
1
LCA
 
1
SRB
 
1
SYC
 
1
SLE
 
1
Other values (261)
261 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique266 ?
Unique (%)100.0%

Sample

1st rowAFG
2nd rowALB
3rd rowDZA
4th rowASM
5th rowAND

Common Values

ValueCountFrequency (%)
AFG1
 
0.4%
LCA1
 
0.4%
SRB1
 
0.4%
SYC1
 
0.4%
SLE1
 
0.4%
SGP1
 
0.4%
SXM1
 
0.4%
SVK1
 
0.4%
SVN1
 
0.4%
SLB1
 
0.4%
Other values (256)256
96.2%

Length

2022-04-02T15:23:36.028196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
afg1
 
0.4%
bhr1
 
0.4%
cpv1
 
0.4%
bdi1
 
0.4%
dza1
 
0.4%
asm1
 
0.4%
and1
 
0.4%
ago1
 
0.4%
atg1
 
0.4%
arg1
 
0.4%
Other values (256)256
96.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1990
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct223
Distinct (%)99.1%
Missing41
Missing (%)15.4%
Infinite0
Infinite (%)0.0%
Mean7.51635057 × 1011
Minimum8824447.74
Maximum2.26992 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:23:36.132074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8824447.74
5-th percentile221880740.8
Q12653480001
median1.274738065 × 1010
Q32.05332 × 1011
95-th percentile3.700848 × 1012
Maximum2.26992 × 1013
Range2.269919118 × 1013
Interquartile range (IQR)2.0267852 × 1011

Descriptive statistics

Standard deviation2.826654845 × 1012
Coefficient of variation (CV)3.76067457
Kurtosis36.59788876
Mean7.51635057 × 1011
Median Absolute Deviation (MAD)1.254595102 × 1010
Skewness5.827730641
Sum1.691178878 × 1014
Variance7.989977612 × 1024
MonotonicityNot monotonic
2022-04-02T15:23:36.258736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.33961 × 10112
 
0.8%
4.07187 × 10112
 
0.8%
2.65987 × 10111
 
0.4%
214727767.61
 
0.4%
9170442281
 
0.4%
1.15552 × 10111
 
0.4%
5.36559 × 10111
 
0.4%
80325511731
 
0.4%
217259259.31
 
0.4%
579629629.61
 
0.4%
Other values (213)213
80.1%
(Missing)41
 
15.4%
ValueCountFrequency (%)
8824447.741
0.4%
39809538.681
0.4%
784760001
0.4%
112119406.51
0.4%
113563821.61
0.4%
125766269.81
0.4%
1472000001
0.4%
168879207.21
0.4%
201429629.61
0.4%
214727767.61
0.4%
ValueCountFrequency (%)
2.26992 × 10131
0.4%
1.8934 × 10131
0.4%
1.8798 × 10131
0.4%
1.835 × 10131
0.4%
8.82356 × 10121
0.4%
6.55867 × 10121
0.4%
6.49847 × 10121
0.4%
5.96314 × 10121
0.4%
5.88069 × 10121
0.4%
4.73721 × 10121
0.4%

2000
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct245
Distinct (%)99.2%
Missing19
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean1.009236625 × 1012
Minimum13742057.05
Maximum3.3816 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:23:36.384399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum13742057.05
5-th percentile403364148.4
Q13058487668
median1.657753336 × 1010
Q32.017525 × 1011
95-th percentile5.841399 × 1012
Maximum3.3816 × 1013
Range3.381598626 × 1013
Interquartile range (IQR)1.986940123 × 1011

Descriptive statistics

Standard deviation3.962904642 × 1012
Coefficient of variation (CV)3.926635778
Kurtosis41.09229162
Mean1.009236625 × 1012
Median Absolute Deviation (MAD)1.603830608 × 1010
Skewness6.140583614
Sum2.492814465 × 1014
Variance1.57046132 × 1025
MonotonicityNot monotonic
2022-04-02T15:23:36.517044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.23088 × 10112
 
0.8%
6.30429 × 10112
 
0.8%
2.916995301 × 10101
 
0.4%
1.633081418 × 10101
 
0.4%
5.96878 × 10111
 
0.4%
1.51753 × 10111
 
0.4%
419933580.91
 
0.4%
2.028962764 × 10101
 
0.4%
635874002.21
 
0.4%
9.607447796 × 10101
 
0.4%
Other values (235)235
88.3%
(Missing)19
 
7.1%
ValueCountFrequency (%)
13742057.051
0.4%
67254174.41
0.4%
1153475001
0.4%
1462975001
0.4%
204849612.61
0.4%
2332718001
0.4%
269019710.31
0.4%
272014693.11
0.4%
333470370.41
0.4%
351136568.41
0.4%
ValueCountFrequency (%)
3.3816 × 10131
0.4%
2.76902 × 10131
0.4%
2.76132 × 10131
0.4%
2.63806 × 10131
0.4%
1.10006 × 10131
0.4%
1.02523 × 10131
0.4%
1.00467 × 10131
0.4%
8.37329 × 10121
0.4%
7.26012 × 10121
0.4%
6.47952 × 10121
0.4%

2011
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct256
Distinct (%)99.2%
Missing8
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean2.333418527 × 1012
Minimum38711810.21
Maximum7.36537 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:23:36.651684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum38711810.21
5-th percentile735925758.4
Q18106590717
median4.687278598 × 1010
Q35.521485 × 1011
95-th percentile1.5849845 × 1013
Maximum7.36537 × 1013
Range7.365366129 × 1013
Interquartile range (IQR)5.440419093 × 1011

Descriptive statistics

Standard deviation7.991606776 × 1012
Coefficient of variation (CV)3.424849286
Kurtosis36.29192562
Mean2.333418527 × 1012
Median Absolute Deviation (MAD)4.582660412 × 1010
Skewness5.52070961
Sum6.020219799 × 1014
Variance6.386577886 × 1025
MonotonicityNot monotonic
2022-04-02T15:23:36.778346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.614 × 10122
 
0.8%
2.27455 × 10122
 
0.8%
936089385.51
 
0.4%
1.47877 × 10121
 
0.4%
1.490730893 × 10101
 
0.4%
4.58202 × 10111
 
0.4%
10499522331
 
0.4%
5.151636666 × 10101
 
0.4%
9.936272049 × 10101
 
0.4%
1.780511312 × 10101
 
0.4%
Other values (246)246
92.5%
(Missing)8
 
3.0%
ValueCountFrequency (%)
38711810.211
0.4%
66055407.671
0.4%
1721885001
0.4%
181705153.61
0.4%
1969111001
0.4%
231489270.11
0.4%
3113016001
0.4%
414523388.51
0.4%
501025925.91
0.4%
5700000001
0.4%
ValueCountFrequency (%)
7.36537 × 10131
0.4%
4.91095 × 10131
0.4%
4.86108 × 10131
0.4%
4.57868 × 10131
0.4%
2.53687 × 10131
0.4%
2.42271 × 10131
0.4%
2.3826 × 10131
0.4%
2.36961 × 10131
0.4%
2.32964 × 10131
0.4%
1.97519 × 10131
0.4%

2012
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct255
Distinct (%)99.2%
Missing9
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean2.405010267 × 1012
Minimum37671774.69
Maximum7.53123 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:23:36.912985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum37671774.69
5-th percentile747471758.2
Q18709165249
median4.658045747 × 1010
Q35.52484 × 1011
95-th percentile1.647958 × 1013
Maximum7.53123 × 1013
Range7.531226233 × 1013
Interquartile range (IQR)5.437748348 × 1011

Descriptive statistics

Standard deviation8.16296171 × 1012
Coefficient of variation (CV)3.394148384
Kurtosis35.58490607
Mean2.405010267 × 1012
Median Absolute Deviation (MAD)4.55646141 × 1010
Skewness5.449014094
Sum6.180876386 × 1014
Variance6.663394387 × 1025
MonotonicityNot monotonic
2022-04-02T15:23:37.047624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.67788 × 10122
 
0.8%
2.30009 × 10122
 
0.8%
985865921.81
 
0.4%
1.32482 × 10121
 
0.4%
1.193147217 × 10101
 
0.4%
4.34401 × 10111
 
0.4%
11909941271
 
0.4%
4.658045747 × 10101
 
0.4%
9.425857269 × 10101
 
0.4%
1.990731707 × 10101
 
0.4%
Other values (245)245
92.1%
(Missing)9
 
3.4%
ValueCountFrequency (%)
37671774.691
0.4%
96927201.481
0.4%
1804363001
0.4%
190243432.81
0.4%
2123978001
0.4%
250680845.71
0.4%
3272487001
0.4%
470714083.41
0.4%
485996296.31
0.4%
6400000001
0.4%
ValueCountFrequency (%)
7.53123 × 10131
0.4%
4.91591 × 10131
0.4%
4.85801 × 10131
0.4%
4.57368 × 10131
0.4%
2.69547 × 10131
0.4%
2.57715 × 10131
0.4%
2.53878 × 10131
0.4%
2.51891 × 10131
0.4%
2.2463 × 10131
0.4%
2.11318 × 10131
0.4%

2013
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct256
Distinct (%)99.2%
Missing8
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean2.482541918 × 1012
Minimum37509075.11
Maximum7.74395 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:23:37.182264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum37509075.11
5-th percentile768296288.3
Q18747773730
median4.981676038 × 1010
Q35.4470925 × 1011
95-th percentile1.7062765 × 1013
Maximum7.74395 × 1013
Range7.743946249 × 1013
Interquartile range (IQR)5.359614763 × 1011

Descriptive statistics

Standard deviation8.388438357 × 1012
Coefficient of variation (CV)3.378971488
Kurtosis34.93592089
Mean2.482541918 × 1012
Median Absolute Deviation (MAD)4.866792423 × 1010
Skewness5.38692594
Sum6.404958148 × 1014
Variance7.036589807 × 1025
MonotonicityNot monotonic
2022-04-02T15:23:37.313911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.78503 × 10122
 
0.8%
2.35961 × 10122
 
0.8%
9.854130546 × 10101
 
0.4%
1.35476 × 10121
 
0.4%
1.842646902 × 10101
 
0.4%
4.00886 × 10111
 
0.4%
45744956671
 
0.4%
12846989221
 
0.4%
4.840189681 × 10101
 
0.4%
2.0146405 × 10101
 
0.4%
Other values (246)246
92.5%
(Missing)8
 
3.0%
ValueCountFrequency (%)
37509075.111
0.4%
98491843.641
0.4%
1848404001
0.4%
185114059.61
0.4%
2211172001
0.4%
300554483.61
0.4%
3172144001
0.4%
450643615.21
0.4%
498296296.31
0.4%
6380000001
0.4%
ValueCountFrequency (%)
7.74395 × 10131
0.4%
4.97509 × 10131
0.4%
4.92306 × 10131
0.4%
4.61909 × 10131
0.4%
2.85276 × 10131
0.4%
2.73175 × 10131
0.4%
2.69221 × 10131
0.4%
2.66069 × 10131
0.4%
2.34775 × 10131
0.4%
2.13676 × 10131
0.4%

2014
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct256
Distinct (%)99.2%
Missing8
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean2.553372863 × 1012
Minimum37290607.54
Maximum7.95577 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:23:37.452562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum37290607.54
5-th percentile823047358.2
Q19297230738
median5.114387552 × 1010
Q35.4562625 × 1011
95-th percentile1.7799015 × 1013
Maximum7.95577 × 1013
Range7.955766271 × 1013
Interquartile range (IQR)5.363290193 × 1011

Descriptive statistics

Standard deviation8.61623976 × 1012
Coefficient of variation (CV)3.374454191
Kurtosis34.72758412
Mean2.553372863 × 1012
Median Absolute Deviation (MAD)4.990651952 × 1010
Skewness5.365227867
Sum6.587701987 × 1014
Variance7.42395876 × 1025
MonotonicityNot monotonic
2022-04-02T15:23:37.589707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.84977 × 10122
 
0.8%
2.58456 × 10122
 
0.8%
1.00955 × 10111
 
0.4%
1.3694 × 10121
 
0.4%
1.396221285 × 10101
 
0.4%
3.81199 × 10111
 
0.4%
50219563211
 
0.4%
13355388931
 
0.4%
4.993068501 × 10101
 
0.4%
2.049712677 × 10101
 
0.4%
Other values (246)246
92.5%
(Missing)8
 
3.0%
ValueCountFrequency (%)
37290607.541
0.4%
104654365.21
0.4%
179703165.41
0.4%
1821428001
0.4%
2416698001
0.4%
3192712001
0.4%
346528329.21
0.4%
4398788281
0.4%
520207407.41
0.4%
6430000001
0.4%
ValueCountFrequency (%)
7.95577 × 10131
0.4%
5.08362 × 10131
0.4%
5.02639 × 10131
0.4%
4.71328 × 10131
0.4%
2.95592 × 10131
0.4%
2.8239 × 10131
0.4%
2.78158 × 10131
0.4%
2.75043 × 10131
0.4%
2.3794 × 10131
0.4%
2.20384 × 10131
0.4%

2015
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct255
Distinct (%)99.2%
Missing9
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean2.407492696 × 1012
Minimum35492074.22
Maximum7.51124 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:23:37.731328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum35492074.22
5-th percentile885591717.2
Q18738203042
median5.006594609 × 1010
Q35.05104 × 1011
95-th percentile1.828524 × 1013
Maximum7.51124 × 1013
Range7.511236451 × 1013
Interquartile range (IQR)4.96365797 × 1011

Descriptive statistics

Standard deviation8.171350073 × 1012
Coefficient of variation (CV)3.394132861
Kurtosis34.29380277
Mean2.407492696 × 1012
Median Absolute Deviation (MAD)4.872925349 × 1010
Skewness5.334055414
Sum6.18725623 × 1014
Variance6.677096202 × 1025
MonotonicityNot monotonic
2022-04-02T15:23:37.868104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.68066 × 10122
 
0.8%
2.69943 × 10122
 
0.8%
12530726261
 
0.4%
1.199780076 × 10101
 
0.4%
3.4671 × 10111
 
0.4%
53317613941
 
0.4%
13070615441
 
0.4%
4.30901734 × 10101
 
0.4%
8.860128984 × 10101
 
0.4%
1.913421176 × 10101
 
0.4%
Other values (245)245
92.1%
(Missing)9
 
3.4%
ValueCountFrequency (%)
35492074.221
0.4%
86529661.371
0.4%
171117816.71
0.4%
1838143001
0.4%
2804577001
0.4%
316066072.31
0.4%
3164899001
0.4%
437006227.21
0.4%
5407370371
0.4%
6730000001
0.4%
ValueCountFrequency (%)
7.51124 × 10131
0.4%
4.80559 × 10131
0.4%
4.74639 × 10131
0.4%
4.46396 × 10131
0.4%
2.77952 × 10131
0.4%
2.67972 × 10131
0.4%
2.63804 × 10131
0.4%
2.57779 × 10131
0.4%
2.19514 × 10131
0.4%
2.05086 × 10131
0.4%

2016
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct254
Distinct (%)99.2%
Missing10
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean2.447400543 × 1012
Minimum36547799.58
Maximum7.63051 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:23:38.008731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum36547799.58
5-th percentile956540137.8
Q18666853262
median4.886912778 × 1010
Q35.26124 × 1011
95-th percentile1.8576425 × 1013
Maximum7.63051 × 1013
Range7.630506345 × 1013
Interquartile range (IQR)5.174571467 × 1011

Descriptive statistics

Standard deviation8.321993836 × 1012
Coefficient of variation (CV)3.400339948
Kurtosis34.24940788
Mean2.447400543 × 1012
Median Absolute Deviation (MAD)4.746652634 × 1010
Skewness5.333893665
Sum6.265345391 × 1014
Variance6.92555814 × 1025
MonotonicityNot monotonic
2022-04-02T15:23:38.145410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5639 × 10122
 
0.8%
2.92645 × 10122
 
0.8%
3.18764 × 10111
 
0.4%
3.23586 × 10111
 
0.4%
55298734801
 
0.4%
13785511181
 
0.4%
4.473633352 × 10101
 
0.4%
8.961405314 × 10101
 
0.4%
12636871511
 
0.4%
1.811656246 × 10101
 
0.4%
Other values (244)244
91.7%
(Missing)10
 
3.8%
ValueCountFrequency (%)
36547799.581
0.4%
99723394.961
0.4%
178328984.11
0.4%
2015109001
0.4%
2983000001
0.4%
3322652001
0.4%
3454956151
0.4%
420540178.61
0.4%
576229629.61
0.4%
6710000001
0.4%
ValueCountFrequency (%)
7.63051 × 10131
0.4%
4.90962 × 10131
0.4%
4.8398 × 10131
0.4%
4.56631 × 10131
0.4%
2.79441 × 10131
0.4%
2.69453 × 10131
0.4%
2.6558 × 10131
0.4%
2.59858 × 10131
0.4%
2.27207 × 10131
0.4%
2.04456 × 10131
0.4%

2017
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct254
Distinct (%)99.2%
Missing10
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean2.620694305 × 1012
Minimum40619251.99
Maximum8.11933 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:23:38.283016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum40619251.99
5-th percentile986784638.4
Q19565594729
median5.332271346 × 1010
Q35.666715 × 1011
95-th percentile1.9795075 × 1013
Maximum8.11933 × 1013
Range8.119325938 × 1013
Interquartile range (IQR)5.571059053 × 1011

Descriptive statistics

Standard deviation8.852539073 × 1012
Coefficient of variation (CV)3.377936547
Kurtosis33.6313348
Mean2.620694305 × 1012
Median Absolute Deviation (MAD)5.180613757 × 1010
Skewness5.273583361
Sum6.708977422 × 1014
Variance7.836744803 × 1025
MonotonicityNot monotonic
2022-04-02T15:23:38.417684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6691 × 10122
 
0.8%
3.34811 × 10122
 
0.8%
3.43338 × 10111
 
0.4%
3.81449 × 10111
 
0.4%
56090000001
 
0.4%
14837589071
 
0.4%
4.846908271 × 10101
 
0.4%
9.515788867 × 10101
 
0.4%
11916201121
 
0.4%
1.875346963 × 10101
 
0.4%
Other values (244)244
91.7%
(Missing)10
 
3.8%
ValueCountFrequency (%)
40619251.991
0.4%
109359680.21
0.4%
187276124.81
0.4%
2132041001
0.4%
2853000001
0.4%
3666668001
0.4%
375614126.21
0.4%
4603791451
0.4%
5198370371
0.4%
6120000001
0.4%
ValueCountFrequency (%)
8.11933 × 10131
0.4%
5.13172 × 10131
0.4%
5.05016 × 10131
0.4%
4.75926 × 10131
0.4%
3.07045 × 10131
0.4%
2.95959 × 10131
0.4%
2.9186 × 10131
0.4%
2.86801 × 10131
0.4%
2.42685 × 10131
0.4%
2.19395 × 10131
0.4%

2018
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct254
Distinct (%)99.2%
Missing10
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean2.786905248 × 1012
Minimum42588164.97
Maximum8.62676 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:23:38.554321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum42588164.97
5-th percentile1037570866
Q11.046232583 × 1010
median5.614403705 × 1010
Q35.6344425 × 1011
95-th percentile2.1045025 × 1013
Maximum8.62676 × 1013
Range8.626755741 × 1013
Interquartile range (IQR)5.529819242 × 1011

Descriptive statistics

Standard deviation9.425222448 × 1012
Coefficient of variation (CV)3.381967312
Kurtosis33.30594895
Mean2.786905248 × 1012
Median Absolute Deviation (MAD)5.454912672 × 1010
Skewness5.247276147
Sum7.134477434 × 1014
Variance8.88348182 × 1025
MonotonicityNot monotonic
2022-04-02T15:23:38.672972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.75454 × 10122
 
0.8%
3.43659 × 10122
 
0.8%
3.75982 × 10111
 
0.4%
4.04842 × 10111
 
0.4%
58506772961
 
0.4%
15745991831
 
0.4%
5.413714215 × 10101
 
0.4%
1.05561 × 10111
 
0.4%
11854748601
 
0.4%
1.805322858 × 10101
 
0.4%
Other values (244)244
91.7%
(Missing)10
 
3.8%
ValueCountFrequency (%)
42588164.971
0.4%
124021393.71
0.4%
200157020.61
0.4%
2215889001
0.4%
2847000001
0.4%
4019323001
0.4%
412253809.71
0.4%
489235527.41
0.4%
550622222.21
0.4%
6390000001
0.4%
ValueCountFrequency (%)
8.62676 × 10131
0.4%
5.45575 × 10131
0.4%
5.34691 × 10131
0.4%
5.04857 × 10131
0.4%
3.26238 × 10131
0.4%
3.14124 × 10131
0.4%
3.09856 × 10131
0.4%
3.04789 × 10131
0.4%
2.64163 × 10131
0.4%
2.36387 × 10131
0.4%

2019
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct251
Distinct (%)99.2%
Missing13
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean2.864963562 × 1012
Minimum47271463.33
Maximum8.75681 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:23:38.796642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum47271463.33
5-th percentile1072697531
Q11.131495134 × 1010
median6.113687369 × 1010
Q35.97281 × 1011
95-th percentile2.203352 × 1013
Maximum8.75681 × 1013
Range8.756805273 × 1013
Interquartile range (IQR)5.859660487 × 1011

Descriptive statistics

Standard deviation9.620595498 × 1012
Coefficient of variation (CV)3.358016704
Kurtosis32.70646963
Mean2.864963562 × 1012
Median Absolute Deviation (MAD)5.944934036 × 1010
Skewness5.198038192
Sum7.248357812 × 1014
Variance9.255585774 × 1025
MonotonicityNot monotonic
2022-04-02T15:23:38.919314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.80448 × 10122
 
0.8%
3.59725 × 10122
 
0.8%
3.74386 × 10111
 
0.4%
1.39305 × 10121
 
0.4%
3.87935 × 10111
 
0.4%
64766745921
 
0.4%
15700932291
 
0.4%
5.417887761 × 10101
 
0.4%
1.05284 × 10111
 
0.4%
1.879945074 × 10101
 
0.4%
Other values (241)241
90.6%
(Missing)13
 
4.9%
ValueCountFrequency (%)
47271463.331
0.4%
118724073.81
0.4%
188391770.61
0.4%
2394622001
0.4%
2742000001
0.4%
4080571001
0.4%
427425039.71
0.4%
512350059.41
0.4%
6115370371
0.4%
6480000001
0.4%
ValueCountFrequency (%)
8.75681 × 10131
0.4%
5.50456 × 10131
0.4%
5.39831 × 10131
0.4%
5.09838 × 10131
0.4%
3.34226 × 10131
0.4%
3.22202 × 10131
0.4%
3.17711 × 10131
0.4%
3.11841 × 10131
0.4%
2.69815 × 10131
0.4%
2.40353 × 10131
0.4%

2020
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct240
Distinct (%)99.2%
Missing24
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean2.892666507 × 1012
Minimum48855550.2
Maximum8.4747 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T15:23:39.050150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum48855550.2
5-th percentile883699682.2
Q11.204995638 × 1010
median6.212830212 × 1010
Q37.4417475 × 1011
95-th percentile2.206317 × 1013
Maximum8.4747 × 1013
Range8.474695114 × 1013
Interquartile range (IQR)7.321247936 × 1011

Descriptive statistics

Standard deviation9.530220636 × 1012
Coefficient of variation (CV)3.294614368
Kurtosis30.93199947
Mean2.892666507 × 1012
Median Absolute Deviation (MAD)6.08254612 × 1010
Skewness5.059810576
Sum7.000252948 × 1014
Variance9.082510537 × 1025
MonotonicityNot monotonic
2022-04-02T15:23:39.421129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.70538 × 10122
 
0.8%
3.38642 × 10122
 
0.8%
2.011613733 × 10101
 
0.4%
1.05173 × 10111
 
0.4%
1.28148 × 10121
 
0.4%
3.35442 × 10111
 
0.4%
69652853251
 
0.4%
15458884261
 
0.4%
5.358960958 × 10101
 
0.4%
40632894501
 
0.4%
Other values (230)230
86.5%
(Missing)24
 
9.0%
ValueCountFrequency (%)
48855550.21
0.4%
114626625.61
0.4%
197508774.31
0.4%
2444624001
0.4%
2577000001
0.4%
4100836001
0.4%
472914469.91
0.4%
488829964.11
0.4%
504214814.81
0.4%
7090000001
0.4%
ValueCountFrequency (%)
8.4747 × 10131
0.4%
5.34611 × 10131
0.4%
5.23927 × 10131
0.4%
4.95945 × 10131
0.4%
3.21393 × 10131
0.4%
3.09932 × 10131
0.4%
3.05353 × 10131
0.4%
2.99006 × 10131
0.4%
2.70974 × 10131
0.4%
2.31507 × 10131
0.4%

Interactions

2022-04-02T15:23:33.691139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:19.450678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:20.900607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:22.219924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:23.483961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:24.770110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:26.274345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:27.547628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:28.756768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:29.975335image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:31.440188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:32.564966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:33.778089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:19.539477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:21.001325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:22.317661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:23.582727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:24.871076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:26.372085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:27.641942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:28.851542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:30.316047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:31.526956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:32.652702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:33.873859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:19.641170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:21.111006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:22.424408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:23.692434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:24.985769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:26.479791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:27.746633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:28.956929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:30.419774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:31.624694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:32.748447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:33.967586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:19.741435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:21.220713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:22.530093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:23.812658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:25.352819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:26.584487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:27.850384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:29.059124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:30.522523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:31.719566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:32.846214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:34.061427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:20.110441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:21.328425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:22.638803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:23.916864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:25.456570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:26.726108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:27.956097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:29.163356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:30.627234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:31.817332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:32.941959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:34.161157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:20.212724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:21.438131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:22.748509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:24.027100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:25.564286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:26.832851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:28.061813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:29.273060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:30.734383image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:31.917063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:33.040663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:34.257896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:20.318997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:21.558226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:22.863202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:24.146776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:25.671537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:26.942124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:28.167508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:29.378752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:30.838825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:32.015362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:33.139429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:34.360599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:20.418120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:21.672307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:22.972265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:24.256491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:25.777755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:27.045385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:28.271230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:29.483651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:30.943516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:32.111137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:33.235173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:34.467312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:20.520329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:21.786967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:23.081975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:24.367195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:25.887950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:27.151128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:28.374975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:29.590364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:31.050203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:32.208078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:33.333880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:34.567045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:20.624079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:21.900424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:23.190657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:24.478892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:25.992986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:27.261475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:28.479700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:29.696053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:31.157940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:32.306852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:33.432637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:34.653815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:20.715097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:22.012102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:23.289896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:24.575633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:26.086707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:27.356220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:28.571449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:29.789831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:31.252661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:32.393618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:33.517548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:34.739613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:20.805824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:22.115594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:23.387145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:24.674370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:26.181479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:27.451937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:28.664243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:29.883550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:31.345441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:32.479753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T15:23:33.604343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-04-02T15:23:39.539840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-02T15:23:39.700382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-02T15:23:39.862976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-02T15:23:40.027507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-02T15:23:35.162481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-02T15:23:35.378905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-02T15:23:35.565435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-02T15:23:35.760911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Country NameCountry Code199020002011201220132014201520162017201820192020
0AfghanistanAFGNaNNaN1.780511e+101.990732e+102.014640e+102.049713e+101.913421e+101.811656e+101.875347e+101.805323e+101.879945e+102.011614e+10
1AlbaniaALB2.028554e+093.480355e+091.289076e+101.231983e+101.277622e+101.322815e+101.138685e+101.186120e+101.301969e+101.515643e+101.540024e+101.488763e+10
2AlgeriaDZA6.204856e+105.479039e+102.000130e+112.090590e+112.097550e+112.138100e+111.659790e+111.600340e+111.700970e+111.749110e+111.717670e+111.450090e+11
3American SamoaASMNaNNaN5.700000e+086.400000e+086.380000e+086.430000e+086.730000e+086.710000e+086.120000e+086.390000e+086.480000e+087.090000e+08
4AndorraAND1.029048e+091.429049e+093.629204e+093.188809e+093.193704e+093.271808e+092.789870e+092.896679e+093.000181e+093.218316e+093.155065e+09NaN
5AngolaAGO1.123628e+109.129635e+091.117900e+111.280530e+111.367100e+111.457120e+111.161940e+111.011240e+111.221240e+111.013530e+118.941719e+105.837598e+10
6Antigua and BarbudaATG4.594704e+088.263704e+081.137637e+091.199948e+091.181448e+091.249733e+091.336693e+091.436585e+091.467978e+091.605944e+091.687533e+091.370281e+09
7ArgentinaARG1.413520e+112.842040e+115.301630e+115.459820e+115.520250e+115.263200e+115.947490e+115.575310e+116.436290e+115.248200e+114.519320e+113.892880e+11
8ArmeniaARM2.256839e+091.911564e+091.014211e+101.061932e+101.112147e+101.160951e+101.055334e+101.054614e+101.152746e+101.245794e+101.361929e+101.264121e+10
9ArubaABW7.648871e+081.873453e+092.549721e+092.534637e+092.727850e+092.790849e+092.962905e+092.983637e+093.092430e+093.202189e+09NaNNaN

Last rows

Country NameCountry Code199020002011201220132014201520162017201820192020
256Post-demographic dividendPST1.835000e+132.638060e+134.578680e+134.573680e+134.619090e+134.713280e+134.463960e+134.566310e+134.759260e+135.048570e+135.098380e+134.959450e+13
257Pre-demographic dividendPRE3.706080e+112.775510e+111.199640e+121.306390e+121.435830e+121.511290e+121.318250e+121.205550e+121.258670e+121.344630e+121.407740e+121.293150e+12
258Small statesSST7.473551e+101.290630e+114.743170e+114.944420e+115.141240e+115.298960e+114.522070e+114.449260e+114.799180e+115.275190e+115.195870e+114.514240e+11
259South AsiaSAS4.071870e+116.304290e+112.274550e+122.300090e+122.359610e+122.584560e+122.699430e+122.926450e+123.348110e+123.436590e+123.597250e+123.386420e+12
260South Asia (IDA & IBRD)TSA4.071870e+116.304290e+112.274550e+122.300090e+122.359610e+122.584560e+122.699430e+122.926450e+123.348110e+123.436590e+123.597250e+123.386420e+12
261Sub-Saharan AfricaSSF3.339610e+114.230880e+111.614000e+121.677880e+121.785030e+121.849770e+121.680660e+121.563900e+121.669100e+121.754540e+121.804480e+121.705380e+12
262Sub-Saharan Africa (excluding high income)SSA3.335930e+114.224690e+111.612940e+121.676820e+121.783700e+121.848430e+121.679280e+121.562470e+121.667570e+121.753000e+121.802900e+121.704320e+12
263Sub-Saharan Africa (IDA & IBRD countries)TSS3.339610e+114.230880e+111.614000e+121.677880e+121.785030e+121.849770e+121.680660e+121.563900e+121.669100e+121.754540e+121.804480e+121.705380e+12
264Upper middle incomeUMC2.588190e+124.312390e+121.789020e+131.915760e+132.057840e+132.121890e+131.992180e+131.982060e+132.193950e+132.363870e+132.403530e+132.315070e+13
265WorldWLD2.269920e+133.381600e+137.365370e+137.531230e+137.743950e+137.955770e+137.511240e+137.630510e+138.119330e+138.626760e+138.756810e+138.474700e+13